The subject matter described herein relates to systems and methods for determining carotid artery intima-media thickness (CIMT).
Cardiovascular disease (CVD) is the number one killer in the United States. Nevertheless, CVD is largely preventable. However, the key is to identify at-risk persons before coronary events occur, so that preventive care can be prescribed appropriately. A noninvasive ultrasonography method that has proven to be valuable for predicting individual CVD risk involves determining a person's carotid artery intima-media thickness (CIMT). Interpretation of CIMT ultrasonographic videos involves three manual operations: 1) selection of end-diastolic ultrasonographic frames (EUFs) in each video; 2) localization of a region of interest (ROI) in each selected EUF; and 3) identification of the intima-media boundaries within each ROI to measure CIMT. With reference to FIG. 1, which illustrates a longitudinal view of the common carotid artery of a human subject in an ultrasonographic B-scan image 100, CIMT is defined as the distance between the lumen-intima interface and the media-adventitia interface, measured approximately 1 cm from the carotid bulb on the far wall of the common carotid artery at the end of the diastole. Therefore, interpretation of a CIMT video involves 3 operations: 1) select 3 EUFs in each video (the cardiac cycle indicator shows to where in the cardiac cycle the current frame in the video corresponds); 2) localize an ROI approximately 1 cm distal from the carotid bulb in the selected EUF; and 3) measure the CIMT within the localized ROI.
These three operations, and in particular, the third step of CIMT measurement, are not only tedious and laborious but also subjective to large inter-operator variability if guidelines are not properly followed. These factors have hindered the widespread utilization of CIMT in clinical practice. To overcome this limitation, what is needed is a new system to accelerate CIMT video interpretation through automation of the operations in a novel, unified framework using machine-based artificial neural networks such as convolutional neural networks (CNNs).